Opposition to artificial intelligence is uniting America’s left and right. Max Tegmark, physicist and chairman of the Future of Life Institute, argues that sentiment across the political spectrum, from Bernie Sanders to Steve Bannon, is turning against a \.
Category: robotics/AI – Page 2
DP21577 The Generative AI Learning Penalty: Evidence from Chinese Secondary Education
Using 30 months of panel data on 26,811 Chinese students in grades 7−−12, we study how generative AI affects homework productivity and learning. The data combine monthly closed-book exams, high-school and college entrance exams, and homework scores and completion time across nine subjects. We exploit staggered AI adoption in a difference-in-differences design. AI adoption raises homework scores by 18% and reduces completion time by 30%, but lowers monthly exam scores by 20% within six months. High-stakes entrance-exam scores fall by 18 and 24%, with the full penalty emerging only after about two years. The losses are largest in social science subjects, followed by STEM and languages, and are especially large for junior students, high-achieving students, and boys. The learning losses are concentrated among roughly 80% of AI users whose behavior is consistent with homework outsourcing, as indicated by exceptionally short homework completion time coupled with high homework scores. AI users who maintain similar homework completion time as non-AI users experience small learning losses.
AI, Quantum And The New Cybersecurity Framework Imperative
Understanding these technologies through the lens of resilience, rather than just innovation, is critical for cybersecurity leaders planning for the coming decade.
The key cybersecurity issue of the coming decade will not prevent every breach. It will be about maintaining trust and resilience in an age of increasing digital interdependence.
Organizations that embrace adaptive risk management, quantum preparedness, responsible AI governance, and resilience-by-design will be well-positioned to succeed in the Acceleration Era. The future belongs not only to the most inventive but also to the most trustworthy and resilient businesses.
Maths is Cooked: AI’s Latest Breakthrough — And What’s Next
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As AI continues to improve its reasoning abilities, mathematicians are increasingly worried about the computer algorithms replacing them. In late May, those fears got even worse when OpenAI revealed that one of its general-purpose reasoning models had written a proof solving a math problem that’s sat unsolved for more than 80 years. But should they actually be worried? Let’s take a look.
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Google Is Mapping the Human Brain… and It Gets Terrifying
Google is using AI to map the human brain, generate synthetic neurons, and speed up one of the most ambitious neuroscience projects ever attempted. But as brain mapping, connectomics, and AI brain decoding move forward, a terrifying question appears: what happens to mental privacy when machines can understand the brain better than we do?
This mini-documentary explores Google’s brain mapping research, synthetic neurons, AI mind decoding, neural privacy, and the future of human thought in the age of artificial intelligence.
CHAPTERS:
00:00 Google’s Brain Mapping Project.
02:00 The Scale of the Human Brain.
04:36 Synthetic Neurons Explained.
06:40 AI Is Already Decoding Thoughts.
10:15 The Rise of Neural Privacy.
14:51 Brain Maps and the Future of AI
17:11 Who Owns Your Mind?
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Welcome to AI Uncovered, your ultimate destination for exploring the fascinating world of artificial intelligence! Our channel delves deep into the latest AI trends and technology, providing insights into cutting-edge AI tools, AI news, and breakthroughs in artificial general intelligence (AGI). We simplify complex concepts, making AI explained in a way that is accessible to everyone.
At AI Uncovered, we’re passionate about uncovering the most captivating stories in AI, including the marvels of ChatGPT and advancements by organizations like OpenAI. Our content spans a wide range of topics, from science news and AI innovations to in-depth discussions on the ethical implications of artificial intelligence. Our mission is to enlighten, inspire, and inform our audience about the rapidly evolving technology landscape.
Google DeepMind AI Discovered a Mathematical Pattern Hidden in Prime Numbers
What exactly did DeepMind find?
Could this discovery help solve longstanding mathematical mysteries?
And what might it mean for cryptography, computing, and our understanding of mathematics itself?
In this video, we explore the science behind the discovery, the role of artificial intelligence in modern research, and why mathematicians around the world are paying close attention.
Whether this breakthrough leads to a revolutionary new theorem or simply a deeper understanding of prime numbers, it demonstrates the growing power of AI to accelerate scientific progress.
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Will AI help solve the greatest unsolved problems in mathematics?
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From Worm to AI: How Control Theory Unlocks Neural Networks
In this video, Dr. Ardavan (Ahmad) Borzou will discuss the control theory in network science and its application in C. elegans \& artificial neural networks. A short history of network science and the basics of control theory will also be reviewed.
Comprehensive Python Checklist (machine learning and more advanced libraries will be covered on a different page):
https://compu-flair.com/blogs/program… Website: www.compu-flair.com Chapters: 00:00 — Introduction 01:52 — Application of control theory in the neural net of worm 03:23 — Networks in Data Science & Seven Bridges of Konigsberg Problem 05:00 — History of network science 06:22 — Basics of control theory 10:23 — Results of applying control theory to the neural net of worm 11:27 — Control theory for artificial neural networks 12:44 — Comprehensive Python checklist for data scientists.
CompuFlair Website:
www.compu-flair.com.
Chapters:
00:00 — Introduction.
01:52 — Application of control theory in the neural net of worm.
03:23 — Networks in Data Science \& Seven Bridges of Konigsberg Problem.
05:00 — History of network science.
06:22 — Basics of control theory.
10:23 — Results of applying control theory to the neural net of worm.
11:27 — Control theory for artificial neural networks.
12:44 — Comprehensive Python checklist for data scientists.
Researchers propose ‘copyleft’ rules for generative AI
The rise of generative artificial intelligence (AI) poses challenges for the free and open-source software (FOSS) community, a global network committed to creating and maintaining publicly available software that anyone can use, modify and share. Many AI models have been built on open-source software but do not reciprocate the transparency that the FOSS community’s principles require, leaving open-source developers uncertain about how these AI tools are using their code.
A study by researchers at Yale’s Digital Ethics Center (DEC) explores a potential solution to this problem based on a concept used in free and open-source software known as “copyleft” licenses—a twist on typical copyright rules that obliges works derived from open-source materials to remain as free and transparent as the original work, rather than relicensing it under more restrictive terms. The study is published in the International Journal Of Law And Information Technology.
The authors propose what they call a Contextual Copyleft AI License (CCAI)—a novel extension of copyleft licensing that would treat generative AI models as derivative works and require AI developers training models on open-source code to make their architecture and training data freely available.
Blender Spotted In Boston Dynamics’ Robot Football Training Pipeline
In time for the FIFA World Cup 2026, step behind the scenes to see how Atlas mastered the difficult Ghost Rabona kick.